# Fast and explainable clustering based on sorting

arXiv: https://arxiv.org/abs/2202.01456 · HTML: https://mlsanity.com/paper/2202.01456/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 78 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [DBSCAN++: Towards fast and scalable density clustering](https://mlsanity.com/paper/1810.13105/) (2018)
- [Quickshift++: Provably Good Initializations for Sample-Based Mean Shift](https://mlsanity.com/paper/1805.07909/) (2018)
- [On the Worst-Case Complexity of TimSort](https://mlsanity.com/paper/1805.08612/) (2018)
- [Pre-treatment of outliers and anomalies in plant data: Methodology and case study of a Vacuum Distillation Unit](https://mlsanity.com/paper/2106.14641/) (2021)

## Built on by (most cited first, 4 of 4)

- [Interpretable Clustering: A Survey](https://mlsanity.com/paper/2409.00743/) (2024)
- [Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions](https://mlsanity.com/paper/2311.15719/) (2023)
- [Performance-Efficiency Trade-off for Fashion Image Retrieval](https://mlsanity.com/paper/2509.24477/) (2025)
- [Fast and explainable clustering in the Manhattan and Tanimoto distance](https://mlsanity.com/paper/2601.08781/) (2026)

## Abstract (arXiv)

We introduce a fast and explainable clustering method called CLASSIX. It consists of two phases, namely a greedy aggregation phase of the sorted data into groups of nearby data points, followed by the merging of groups into clusters. The algorithm is controlled by two scalar parameters, namely a distance parameter for the aggregation and another parameter controlling the minimal cluster size. Extensive experiments are conducted to give a comprehensive evaluation of the clustering performance on synthetic and real-world datasets, with various cluster shapes and low to high feature dimensionality. Our experiments demonstrate that CLASSIX competes with state-of-the-art clustering algorithms. The algorithm has linear space complexity and achieves near linear time complexity on a wide range of problems. Its inherent simplicity allows for the generation of intuitive explanations of the computed clusters.